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International Conference on Intelligent Systems Design and Applications

ISDA 2016: Intelligent Systems Design and Applications pp 582–591Cite as

Fishing Monitor System Data: A Naïve Bayes Approach

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

Abstract

This paper discusses the results of an applied research on the fishing activity based on a monitor system developed by a company and fishing reports produced at the end of each fishing activity. Due to economic interests combined with fishing limitations there is a natural tendency for wrong reporting. We apply Data Mining (DM) methodologies to find fishing patterns. These DM techniques in SQL tool allow to deal with the high volume of this data set and determine the major factors that influence fishing activity.

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References

  1. MacLennan, J., Crivat, B., Tang, Z.: Data Mining with Microsoft SQL Server 2008–2009. Wiley Publishing, Indiana (2009). ISBN 978-0-470-27774-4

    Google Scholar 

  2. Lachev, T.: Applied Microsoft Power BI: Bring Your Data to Life. Prologika (2015). ISBN13: 9780976635369

    Google Scholar 

  3. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), , 881–892 (2002)

    Article  MATH  Google Scholar 

  4. Winston, W.L.: Microsoft Excel 2013 Data Analysis and Business Modeling. O’ Reilly Media, inc. (2014). ISBN: 978-0-7356-6913-0

    Google Scholar 

  5. Azevedo, A., Santos, M.F.: KDD, SEMMA and CRISP-DM: a parallel overview. In: Proceedings of the IADIS European Conference on Data Mining 2008, pp. 182–185 (2008)

    Google Scholar 

  6. Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. In: Conference on Neural Information Processing Systems (2002)

    Google Scholar 

  7. Webb, G.I., Boughton, J., Wang, Z.: Not so Naive Bayes: aggregating one-dependence estimators. Mach. Learn. 58(1), 5–24 (2005). Springer

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Business Research Unit (BRU-IUL), Instituto Universitário de Lisboa (ISCTE-IUL), Lisboa, Portugal

    Joao C. Ferreira

  2. ISEL - Instututo Superior de Engenharia de Lisboa, Lisbon, Portugal

    Serge Lage & Iola Pinto

  3. Centro Algoritmi, Universidade Minho, Guimaraes, Portugal

    Joao C. Ferreira

  4. Centro de Matemática e Aplicações, FCT/UNL, Lisbon, Portugal

    Iola Pinto

  5. Xsealance - Sea Technologies SA, Leira, Portugal

    Nuno Antunes

Authors
  1. Joao C. Ferreira
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  2. Serge Lage
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  3. Iola Pinto
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  4. Nuno Antunes
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Corresponding author

Correspondence to Joao C. Ferreira .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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© 2017 Springer International Publishing AG

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Cite this paper

Ferreira, J.C., Lage, S., Pinto, I., Antunes, N. (2017). Fishing Monitor System Data: A Naïve Bayes Approach. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_57

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_57

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

  • eBook Packages: EngineeringEngineering (R0)

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